The system essentially uses an approach similar to StackGAN, but rather than just fix itself to two stages, it progressively grows multiple stages learning a smaller network into a larger one. Using residual layers as a transition step between incrementally larger networks:

Let me add a bit of commentary about this development. The objective of the researchers was definitely not to create better eye candy, but rather to scale the current GAN method to larger images. The application of this method is clearly in its ability to create simulations. Creating simulations will have value in more complex predictive problems. The more accurate simulations that can be generated quickly, the more accurate we can create probabilistic models.

The scary thing though is that these are generated images. The best CGI cannot match these in terms of fooling the brain into believing it to be real. The Star Wars movie “Rogue One” used a CGI image of the late Peter Cushing. I am sure the animators through every trick in the book to make the CGI appear real, but clearly that all failed. However, now you have an automation that can generate high resolutions images realistic images of people. In short, in the next year or two, the movie and games that you see that simulate humans will now finally look like real life. Let that thought sink in for a while.

Last year, I prematurely declared the winner of the best paper at ICLR: “Re-Thinking Generalization”. ICLR has decided to use a double blind paper submission to make the the process more objective. Unfortunately, it is obvious now who deserves to be the hands down winner of the 2018 ICLR conference. That’s unless you can dig up a more impressive submission in the next following months. Good luck mining ICLR submissions!

Deep Learning technology are intuition machines and as a consequence, their ability to generate what we see in a manner that fools our own intuition in indeed uncanny.